algebra

Computational Fluid Dynamics 2021-01-27 – 2023-11-30

Learning graphical models from time series Also, causal discovery, structure discovery in time series 2017-09-20 – 2023-11-28

Automatic differentiation 2016-07-27 – 2023-11-15

Causal inference in highly parameterized ML 2020-09-18 – 2023-10-24

Learning under distribution shift Also transfer learning, covariate shift, transferable learning, domain adaptation, transportability etc 2020-10-17 – 2023-10-24

Numerical PDE solvers 2016-03-01 – 2023-10-23

Orthonormal and unitary matrices Energy preserving operators, generalized rotations 2019-10-22 – 2023-09-19

Differential geometry, geometric algebra etc 2014-11-27 – 2023-09-19

Potential theory in probability Something about harmonic functions or whatever 2020-02-12 – 2023-08-24

Ablation studies and lesion studies In order to understand it we must be able to break it 2016-10-26 – 2023-08-01

Factor graphs 2019-12-16 – 2023-07-31

Matrix calculus 2018-07-09 – 2023-07-24

(Reproducing) kernel tricks 2014-08-18 – 2023-07-21

The Gaussian distribution The default probability distribution 2016-06-27 – 2023-07-19

Hypothesis tests, statistical 2014-08-23 – 2023-07-18

Gaussian process ensembles Bayesian committee machines, product-of-experts 2023-07-12

Matrix norms, divergences, metrics 2016-06-03 – 2023-05-29

Belief propagation 2014-11-25 – 2023-05-17

Canonical correlation 2014-08-23 – 2023-05-17

Jax Julia for python 2020-09-15 – 2023-05-12

Distances between Gaussian distributions Nearly equivalent to distances between symmetric positive definite matrices 2016-06-27 – 2023-05-03

Covariance estimation Esp Gaussian 2014-11-16 – 2023-04-26

Optimal rotations 2021-05-18 – 2023-04-06

Mathematica 2011-04-06 – 2023-04-05

Tensor decompositions 2016-08-15 – 2023-02-21

External validity When does what I learn on one data set apply to another? 2020-10-17 – 2023-02-16

The Matrix-Gaussian distribution 2022-08-19 – 2023-02-16

Last-layer Bayes neural nets Bayesian and other probabilistic inference in overparameterized ML 2017-01-11 – 2023-02-09

Visualising probabilistic graphical models Also related models, such as Neural nets 2018-03-29 – 2023-02-02

Variational message-passing algorithms in graphical models Cleaving reality at the joint, then summing it at the marginal 2014-11-25 – 2023-01-12

Elliptical distributions 2015-06-23 – 2023-01-03

Graph neural nets 2020-09-16 – 2022-12-19

Adverse advice selection 2022-01-25 – 2022-11-15

Interaction effects and subgroups are probably what we want to estimate 2022-01-25 – 2022-11-06

Randomised linear algebra 2016-08-16 – 2022-10-22

Neural tangent kernel 2020-12-09 – 2022-10-14

Precision matrix estimation Especially Gaussain 2014-11-16 – 2022-10-04

Learning graphical models from data Also, causal discovery, structure discovery 2017-09-20 – 2022-10-01

Conditional expectation and probability 2020-02-04 – 2022-09-21

Gaussian belief propagation Least squares at maximal elaboration 2014-11-25 – 2022-09-01

Elliptical belief propagation Generalized least generalized squares 2022-08-22 – 2022-08-23

Automatic differentiation in Julia 2016-07-27 – 2022-08-09

Causal inference on DAGs Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-08-06

Instumental variables and two stage regression Confounding! This scientist performed a miracle graph surgery intervention and you won’t believe what happened next 2016-10-26 – 2022-08-06

Bayes linear regression and basis-functions in Gaussian process regression a.k.a Fixed Rank Kriging, weight space GPs 2022-02-22 – 2022-07-27

Partial differential equations 2021-01-27 – 2022-07-23

Causal graphical model reading group 2022 Causal inference 2022-04-01 – 2022-04-29

SLAM Simultaneous Location and Mapping 2014-11-25 – 2022-04-28

Vecchia factoring of GP likelihoods Ignore some conditioning in the dependencies and attain a sparse cholesky factor for the precision matrix 2022-04-27

Beta Processes 2019-10-14 – 2022-04-08

Stationary Gamma processes 2019-10-14 – 2022-04-08

Particle belief propagation Graphical inference using empirical distribution estimates 2014-07-25 – 2022-04-08

Particle Markov Chain Monte Carlo Particle systems as MCMC proposals 2014-07-25 – 2022-04-08

Partition-valued random variates 2022-04-01

Multivariate Gamma distributions 2019-10-14 – 2022-03-14

Fun with rotational symmetries 2021-01-29 – 2022-03-03

Learning with conservation laws, invariances and symmetries 2020-04-11 – 2022-02-25

Probabilistic graphical models 2014-08-05 – 2022-02-08

Random graphical models Causality in amongst confusion 2021-10-26 – 2022-02-07

Matrix-valued random variates 2021-12-01 – 2022-01-06

Causality via potential outcomes Neyman-Rubin, counterfactuals, conditional treatment effects, and related tricks 2016-10-26 – 2021-12-10

Random rotations 2021-05-18 – 2021-12-01

Gaussian Processes as stochastic differential equations Imposing time on things 2019-09-18 – 2021-11-25

Spectral graph theory Linear signals on graphs 2014-11-24 – 2021-10-27

Neural music synthesis 2016-01-15 – 2021-10-14

Fun with determinants Especially Jacobian determinants 2011-04-06 – 2021-10-12

Multilinear algebra Outer products, tensors, einstein summation 2021-10-08

Arpeggiate by numbers Workaday automatic composition and sequencing 2015-01-07 – 2021-09-20

Algebraic probability If you liked it then you prob’ly put a ring on it 2017-06-15 – 2021-06-24

Isotropic random vectors 2011-08-10 – 2021-05-24

Randomized low dimensional projections 2021-03-12 – 2021-05-24

Infinite width limits of neural networks 2020-12-09 – 2021-05-11

Computational symbolic mathematics Cheating at calculus exams 2016-10-13 – 2021-05-11

Tensor regression 2020-11-19

Efficient factoring of GP likelihoods 2020-10-16 – 2020-10-26

GP inducing features 2020-10-16 – 2020-10-26

GP inducing variables 2020-10-16 – 2020-10-26

Localized Gaussian processes 2020-10-16 – 2020-10-26

Independence, conditional, statistical 2016-04-21 – 2020-09-13

Causal graphical model reading group 2020 An introduction to conditional independence DAGs and their use for causal inference. 2020-08-30 – 2020-09-03

Causal Bayesian networks Staged tree models, probability trees …Causalan Bayesian networks 2020-11-01 – 2020-09-01

Mathematics of textiles 2019-10-14 – 2020-08-20

MAPLE An OK computer algebra system 2020-05-19

Directed graphical models 2017-09-20 – 2020-05-13

Analysis/resynthesis of audio 2016-01-15 – 2020-04-09

Kernel approximation 2016-07-27 – 2020-03-06

Audio source separation 2019-11-04 – 2019-11-26

Tunings 2015-10-29 – 2019-11-18

Undirected graphical models 2017-09-20 – 2019-10-28

Random (element) matrix theory 2014-11-09 – 2019-10-10

Probability 2011-07-29 – 2019-07-15

Inner product spaces The most highly developed theory of squaring things 2019-01-01 – 2019-02-11

Normed spaces 2019-01-01 – 2019-01-04

Linear algebra If the thing is twice as big, the transformed version of the thing is also twice as big. {End} 2011-04-06 – 2018-08-07

Musical metrics and manifolds 2014-09-26 – 2017-03-27

Functional equations Putting the funk in functions 2017-02-06 – 2017-02-19

Category theory 2011-11-25 – 2016-04-18

Expectation maximisation 2014-08-17 – 2016-04-17

Coarse graining 2014-11-11 – 2015-12-02

Sigma algebras, probability spaces, measure theory The scaffolding of randomness 2015-06-20

Algebra I would like to learn 2014-10-15 – 2014-11-22